Description

The R package tests currently have the following warnings which seem problematic. Contributions to fix these would be welcomed!

[LightGBM] [Warning] Unknown parameter: valids
[LightGBM] [Warning] Unknown parameter: 0x0000000016067830>
[LightGBM] [Warning] Unknown parameter: valids
[LightGBM] [Warning] Unknown parameter: 0x0000000016067830>
[LightGBM] [Warning] Unk

Not a high-priority at all, but it'd be more sensible for such a tutorial/testing utility corpus to be implemented elsewhere - maybe under /test/ or some other data- or doc- related module – rather than in gensim.models.word2vec.

Originally posted by @gojomo in RaRe-Technologies/gensim#2939 (comment)

Problem: the approximate method can still be slow for many trees
catboost version: master
Operating System: ubuntu 18.04
CPU: i9
GPU: RTX2080

Would be good to be able to specify how many trees to use for shapley. The model.predict and prediction_type versions allow this. lgbm/xgb allow this.

Right now, pages created by in-memory http driver does not support navigation between pages due to their static nature.
It makes it problematic to "move" from one static page to another within a same domain simply because we cannot reuse current page. In order to do so, we have to create a new page every time we need to move from A to B.
Let's add a possibility to reuse existing page by imp

The official instructions say to use joblib for pickling PyOD models.

This fails for AutoEncoders, or any other TensorFlow-backed model as far as I can tell. The error is:

>>> dump(model, 'model.joblib')
...
TypeError: can't pickle _thread.RLock objects

Note that it's not sufficient to save the underlying Keras S

Contributors: @RNKuhns, @fkiraly, @mloning

Related issue: #718

Estimators

• new estimator scitype: DirectionalForecaster
• reducer from Forecaster to DirectionalForecaster via time-series/tabular classification
• reducer from Forecaster to DirectionalForecaster via output binning
• override score in the reduced forecasters with the "right" binned losses!

y_train, y_tes